def augument(data_path, label, image_name, save_path, size=224, training = True):
image_path = os.path.join(data_path, image_name)
(name, extension) = splitfilename(image_name)
extension = extension.lower()
if extension not in IMG_EXTS:
print('filered image: %s' % image_name)
return
try:
img = image.imdecode(open(image_path, 'rb').read()).astype('float32')
except Exception as ex:
print("error: ", ex)
return
if label is not None:
label_path = os.path.join(save_path, label)
else:
label_path = save_path
mkdir(label_path)
if training:
aug1 = image.HorizontalFlipAug(0.5)
aug2 = image.HorizontalFlipAug(.5)
img = image.resize_short(img, size=384, interp=2)
center_crop, _ = image.center_crop(img, size=(size, size))
new_name = "%s_%s%s" % (name, "0", extension)
cv.imwrite(os.path.join(label_path, new_name), center_crop.asnumpy())
random_crop, _ = image.random_crop(img, size=(size, size))
new_name = "%s_%s%s" % (name, "1", extension)
cv.imwrite(os.path.join(label_path, new_name), random_crop.asnumpy())
random_crop, _ = image.random_crop(img, size=(size, size))
new_name = "%s_%s%s" % (name, "2", extension)
cv.imwrite(os.path.join(label_path, new_name), random_crop.asnumpy())
random_crop, _ = image.random_crop(img, size=(size, size))
new_name = "%s_%s%s" % (name, "3", extension)
cv.imwrite(os.path.join(label_path, new_name), random_crop.asnumpy())
img_aug1 = aug1(random_crop).clip(0,255)
new_name = "%s_%s%s" % (name, "4", extension)
cv.imwrite(os.path.join(label_path, new_name), img_aug1.asnumpy())
img_aug2 = aug2(center_crop).clip(0, 255)
new_name = "%s_%s%s" % (name, "5", extension)
cv.imwrite(os.path.join(label_path, new_name), img_aug2.asnumpy())
img_resize = image.imresize(img, w=size, h=size, interp=2)
new_name = "%s_%s%s" % (name, "6", extension)
cv.imwrite(os.path.join(label_path, new_name), img_resize.asnumpy())
else:
img = image.resize_short(img, size=size)
img, _ = image.center_crop(img, size=(size, size))
new_name = "%s%s" % (name, extension)
cv.imwrite(os.path.join(label_path, new_name), img.asnumpy())
def predict_with_models_from_gluon_model_zoo_example():
# Gluon model zoo provides multiple pre-trained powerful models.
# We can download and load a pre-trained ResNet-50 V2 model that was trained on the ImageNet dataset.
net = models.resnet50_v2(pretrained=True)
# Download and load the text labels for each class.
url = 'http://data.mxnet.io/models/imagenet/synset.txt'
fname = download(url)
with open(fname, 'r') as f:
text_labels = [' '.join(l.split()[1:]) for l in f]
# Randomly pick a dog image from Wikipedia as a test image, download and read it.
url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Golden_Retriever_medium-to-light-coat.jpg/365px-Golden_Retriever_medium-to-light-coat.jpg'
fname = download(url)
x = image.imread(fname)
# Use the image processing functions provided in the MXNet image module.
x = image.resize_short(x, 256)
x, _ = image.center_crop(x, (224, 224))
plt.imshow(x.asnumpy())
plt.show()
def transform(data):
data = data.transpose((2, 0, 1)).expand_dims(axis=0)
rgb_mean = nd.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))
rgb_std = nd.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))
return (data.astype('float32') / 255 - rgb_mean) / rgb_std
prob = net(transform(x)).softmax()
idx = prob.topk(k=5)[0]
for i in idx:
i = int(i.asscalar())
print('With prob = %.5f, it contains %s' %
(prob[0, i].asscalar(), text_labels[i]))
def transform_predict(im):
im = im.astype('float32') / 255
im = image.resize_short(im, 324)
im = nd.transpose(im, (2, 0, 1))
im = mx.nd.image.normalize(im, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
im = ten_crop(im, (299, 299))
return (im)
def transform_cropped_img(im):
im = im.astype('float32') / 255
im = image.resize_short(im, 256)
im = nd.transpose(im, (2,0,1))
im = mx.nd.image.normalize(im, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
im = ten_crop(im, (224, 224))
return (im)
def transform_val(data, label):
im = data.astype('float32') / 255
im = image.resize_short(im, 256)
im, _ = image.center_crop(im, (224, 224))
im = nd.transpose(im, (2,0,1))
im = mx.nd.image.normalize(im, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
return (im, nd.array([label]).asscalar())
def transform_val(data, label):
im = data.astype('float32') / 255
im = image.resize_short(im, 256) #对数据按照短边进行crop为256*256
im, _ = image.center_crop(im, (224, 224)) #对数据进行中心裁剪为224*224
im = nd.transpose(im, (2,0,1)) #
im = mx.nd.image.normalize(im, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))#归一化操作
return (im, nd.array([label]).asscalar()) #返回图像和标签
def main():
net = models.resnet50_v2(pretrained=True)
url = 'http://data.mxnet.io/models/imagenet/synset.txt'
fname = download(url)
with open(fname, 'r') as f:
text_labels = [' '.join(l.split()[1:]) for l in f]
url2 = 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/\
Golden_Retriever_medium-to-light-coat.jpg/\
365px-Golden_Retriever_medium-to-light-coat.jpg'
fname2 = download(url2)
x = image.imread(fname2)
x = image.resize_short(x, 256)
x, _ = image.center_crop(x, (224, 224))
plt.imshow(x.asnumpy())
plt.show()
prob = net(transform(x)).softmax()
idx = prob.topk(k=5)[0]
for i in idx:
i = int(i.asscalar())
print('With prob = %.5f, it contains %s' %
(prob[0, i].asscalar(), text_labels[i]))
def transform_image(img_path):
img = image.imread(img_path)
data = image.resize_short(img, 256)
data, _ = image.center_crop(data, (224, 224))
data = data.transpose((2, 0, 1)).expand_dims(axis=0)
rgb_mean = nd.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))
rgb_std = nd.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))
data = (data.astype("float32") / 255 - rgb_mean) / rgb_std
return data
def transform_predict(im):
im = im.astype('float32') / 255
im = image.resize_short(im, 256)
im = nd.transpose(im, (2, 0, 1))
# im = mx.nd.image.normalize(im, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
# im = mx.image.color_normalize(im, mean=nd.array([[[0.485]], [[0.456]], [[0.406]]]), std=nd.array([[[0.229]], [[0.224]], [[0.225]]]))
im = mx.image.color_normalize(im, mean=0.485, std=0.229)
im = ten_crop(im, (224, 224))
return (im)
def transform_predict(im, size):
im = im.astype('float32') / 255
im = image.resize_short(im, size, interp=1)
# im = image.resize_short(im, 331)
im = nd.transpose(im, (2,0,1))
im = mx.nd.image.normalize(im, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
# im = forty_crop(im, (352, 352))
im = ten_crop(im, (448, 448))
return (im)
def __getitem__(self, idx):
raw_line = self.raw_label[idx]
img_path, bbox = raw_line['img_path'], raw_line['bbox']
raw_image = image.imread(img_path)
raw_image = image.resize_short(raw_image, BASE_SHAPE)
if self.dataset_type == 'train':
raw_image = image.HorizontalFlipAug(0.5)(raw_image)
raw_image = normalize_image(raw_image)
data = raw_image.transpose((2,0,1))
return data, nd.array([raw_line['argmax_index_label']]), nd.array(raw_line['hinge_label'])
def get_img(self):
img_path = raw_img_path.as_posix()
raw_image = image.imread(img_path)
raw_mask = nd.zeros((raw_image.shape[0], raw_image.shape[1], 1)).astype(np.uint8)
concated_data = nd.zeros((raw_image.shape[0], raw_image.shape[1], 4))
raw_mask[bbox[0]:width, bbox[1]:height] = 255
mask_raw_img = nd.concat(raw_image, raw_mask, dim=2)
norm_mask_raw_img = normalize_image(mask_raw_img)
resized_norm_mask_raw_img = image.resize_short(norm_mask_raw_img, BASE_SHAPE)
data = resized_norm_mask_raw_img.transpose((2,0,1))
def load_image(img_path, long_side_length):
x = image.imread(img_path)
x = image.resize_short(x, long_side_length)
x, _ = image.center_crop(x, (448, 448))
x = x.astype('float32')
x = x / 255
x = image.color_normalize(x,
mean=nd.array([0.485, 0.456, 0.406]),
std=nd.array([0.229, 0.224, 0.225]))
x = x.reshape((1, 3, 448, 448))
return x
def classify(image_file_path):
x = image.imread(image_file_path)
x = image.resize_short(x, 256)
x, _ = image.center_crop(x, (224, 224))
plt.imshow(x.asnumpy())
plt.show()
prob = net(transform(x)).softmax()
idx = prob.topk(k=5)[0]
print(' prob | name')
print(' ------------------')
for i in idx:
i = int(i.asscalar())
print(' %.3f | %s' % (prob[0, i].asscalar(), text_labels[i]))
def transformPredict(img):
'''
Input an image, transform and random crop the image, return images after processed.
输入一张图片，对其进行数据形式转换和随机剪裁堆叠后返回。
:param img: image data, mx.ndarray, h*w*c
:return img: imafe data, mx.ndarray, b*c*h*w
'''
img = img.astype('float32') / 255 # 0-255 to 0-1
img = image.resize_short(img, 120) # resize
img = nd.transpose(
img, (2, 0, 1)) # channel transpose to batch * channel * h * w
img = mx.nd.image.normalize(img, mean=(0.5, 0.5, 0.5),
std=(0.2, 0.2, 0.2)) # normalize
img = sixCrop(img, (112, 112)) # random crop
return img
def transform_filter(data, label):
"""
用于图片/标签前处理的函数。
Transform function for image/label pre-processing.
:param data: image data, mx.ndarray
:param label: image label, mx.ndarray
"""
im = filter_image(data, filter_level) # compress image to certain bit
im = im.astype("float32") / 255
im = image.resize_short(im, RESIZE_SIZE)
im, _ = image.center_crop(im, (INPUT_SIZE, INPUT_SIZE))
im = nd.transpose(im, (2, 0, 1))
im = mx.nd.image.normalize(im,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
return (im, nd.array([label]).asscalar())
def transform(data, label):
'''
Function that converts "data"" into the input image tensor for a CNN
Label is converted into a float tensor.
'''
image = mx.nd.array(data).expand_dims(axis=2)
image = resize_short(image, int(800 / 3))
image = image.transpose([2, 0, 1]) / 255.
label = label[0].astype(np.float32)
bb = label.copy()
new_w = (1 + expand_bb_scale) * bb[2]
new_h = (1 + expand_bb_scale) * bb[3]
bb[0] = bb[0] - (new_w - bb[2]) / 2
bb[1] = bb[1] - (new_h - bb[3]) / 2
bb[2] = new_w
bb[3] = new_h
return image, mx.nd.array(bb)
def transform(image, label):
'''
Function that converts resizes image into the input image tensor for a CNN.
The labels (bounding boxes) are expanded, converted into (x, y, x+w, y+h), and
zero padded to the maximum number of labels. Finally, it is converted into a float
tensor.
'''
max_label_n = 128 if detection_box == "word" else 13
# Resize the image
image = np.expand_dims(image, axis=2)
image = mx.nd.array(image)
image = resize_short(image, image_size)
image = image.transpose([2, 0, 1])/255.
# Expand the bounding box by expand_bb_scale
bb = label.copy()
new_w = (1 + expand_bb_scale) * bb[:, 2]
new_h = (1 + expand_bb_scale) * bb[:, 3]
bb[:, 0] = bb[:, 0] - (new_w - bb[:, 2])/2
bb[:, 1] = bb[:, 1] - (new_h - bb[:, 3])/2
bb[:, 2] = new_w
bb[:, 3] = new_h
label = bb
# Convert the predicted bounding box from (x, y, w, h to (x, y, x + w, y + h)
label = label.astype(np.float32)
label[:, 2] = label[:, 0] + label[:, 2]
label[:, 3] = label[:, 1] + label[:, 3]
# Zero pad the data
label_n = label.shape[0]
label_padded = np.zeros(shape=(max_label_n, 5))
label_padded[:label_n, 1:] = label
label_padded[:label_n, 0] = np.ones(shape=(1, label_n))
label_padded = mx.nd.array(label_padded)
return image, label_padded
def transform(image, bbox, text):
'''
Function that converts resizes image into the input image tensor for a CNN.
The bounding boxes are expanded, and
zero padded to the maximum number of labels. Finally, it is converted into a float
tensor.
'''
max_label_n = 128
# Resize the image
image = np.expand_dims(image, axis=2)
image = mx.nd.array(image)
image = resize_short(image, image_size)
image = image.transpose([2, 0, 1]) / 255.
# Expand the bounding box by expand_bb_scale
bb = bbox.copy()
new_w = (1 + expand_bb_scale) * bb[:, 2]
new_h = (1 + expand_bb_scale) * bb[:, 3]
bb[:, 0] = bb[:, 0] - (new_w - bb[:, 2]) / 2
bb[:, 1] = bb[:, 1] - (new_h - bb[:, 3]) / 2
bb[:, 2] = new_w
bb[:, 3] = new_h
bbox = bb
bbox = bbox.astype(np.float32)
# Zero pad the data
label_n = bbox.shape[0]
label_padded = np.zeros(shape=(max_label_n, 5))
label_padded[:label_n, 1:] = bbox
label_padded[:label_n, 0] = np.ones(shape=(1, label_n))
label_padded = mx.nd.array(label_padded)
return image, label_padded
def load_vgg16_image(img_path, image_width=224, image_height=224):
x = image.imread(img_path)
x = image.resize_short(x, 256)
x, _ = image.center_crop(x, (image_width, image_height))
return x
from mxnet.gluon.model_zoo import vision as models
from mxnet.gluon.utils import download
from mxnet import image
net = models.resnet50_v2(pretrained=True)
url = 'http://data.mxnet.io/models/imagenet/synset.txt'
fname = download(url)
with open(fname, 'r') as f:
text_labels = [' '.join(l.split()[1]) for l in f]
url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Golden_Retriever_medium-to-light-coat.jpg/365px-Golden_Retriever_medium-to-light-coat.jpg'
fname = download(url)
x = image.imread(fname)
x = image.resize_short(x, 256)
x, _ = image.center_crop(x, (224, 224))
plt.imshow(x.asnumpy())
plt.show()
def transform(data):
data = data.transpose((2, 0, 1)).expand_dims(axis=0)
rgb_mean = nd.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))
rgb_std = nd.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))
return (data.astype('float32') / 255 - rgb_mean) / rgb_std
prob = net(transform(x)).softmax()
idx = prob.topk(k=5)[0]
for i in idx:
def load_vgg16_image(img_path):
x = image.imread(img_path)
x = image.resize_short(x, 256)
x, _ = image.center_crop(x, (224, 224))
return x
def transform_fully_img(im):
im = image.resize_short(im, BASE_SHAPE)
im = normalize_image(im)
im = im.transpose((2,0,1))
im = im.expand_dims(axis=0)
return (im)
print(Essex_test_files)
saved_path = r'op/'
# Essex_test_files = glob.glob(r'I:\t\depthmap\*.jpg')
Essex_test_files = glob.glob(r'photos/*.jpg')
print("len of Essex_test_files:", len(Essex_test_files))
import random
print("before sampling:", len(Essex_test_files))
Essex_test_files = random.sample(Essex_test_files, len(Essex_test_files))
print("after sampling:", len(Essex_test_files))
for idx, filename in enumerate(Essex_test_files):
try:
img = image.imread(filename)
img = image.resize_short(img, 1024)
# img = image.resize_short(img, 100)
print("filename: ", filename)
# ctx = mx.gpu(0)
img = test_transform(img, ctx)
# print("img: ", img)
output = model.predict(img)
# print("output: ", output)
predict = mx.nd.squeeze(mx.nd.argmax(output, 1)).asnumpy()
# print("predict: ", predict)
def main():
start = timer()
print('Processing Start time: %.1f' % (start))
print("current time", datetime.now())
gauth = GoogleAuth()
gauth.LocalWebserverAuth()
drive = GoogleDrive(gauth)
# Auto-iterate through all files that matches this query
file_list = drive.ListFile({'q': "'root' in parents"}).GetList()
for file in file_list:
# print('title: {}, id: {}'.format(file1['title'], file1['id']))
file_id = None
if file['title'] == "semanticsegmentation":
print('Folder Found')
file_id = file['id']
break
if file_id is not None:
classes = ["wall","building;edifice","sky","floor;flooring","tree","ceiling","road;route","bed","windowpane;window","grass","cabinet","sidewalk;pavement","person;individual;someone;somebody;mortal;soul","earth;ground","door;double;door","table","mountain;mount","plant;flora;plant;life","curtain;drape;drapery;mantle;pall","chair","car;auto;automobile;machine;motorcar","water","painting;picture","sofa;couch;lounge","shelf","house","sea","mirror","rug;carpet;carpeting","field","armchair","seat","fence;fencing","desk","rock;stone","wardrobe;closet;press","lamp","bathtub;bathing;tub;bath;tub","railing;rail","cushion","base;pedestal;stand","box","column;pillar","signboard;sign","chest;of;drawers;chest;bureau;dresser","counter","sand","sink","skyscraper","fireplace;hearth;open;fireplace","refrigerator;icebox","grandstand;covered;stand","path","stairs;steps","runway","case;display;case;showcase;vitrine","pool;table;billiard;table;snooker;table","pillow","screen;door;screen","stairway;staircase","river","bridge;span","bookcase","blind;screen","coffee;table;cocktail;table","toilet;can;commode;crapper;pot;potty;stool;throne","flower","book","hill","bench","countertop","stove;kitchen;stove;range;kitchen;range;cooking;stove","palm;palm;tree","kitchen;island","computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system","swivel;chair","boat","bar","arcade;machine","hovel;hut;hutch;shack;shanty","bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle","towel","light;light;source","truck;motortruck","tower","chandelier;pendant;pendent","awning;sunshade;sunblind","streetlight;street;lamp","booth;cubicle;stall;kiosk","television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box","airplane;aeroplane;plane","dirt;track","apparel;wearing;apparel;dress;clothes","pole","land;ground;soil","bannister;banister;balustrade;balusters;handrail","escalator;moving;staircase;moving;stairway","ottoman;pouf;pouffe;puff;hassock","bottle","buffet;counter;sideboard","poster;posting;placard;notice;bill;card","stage","van","ship","fountain","conveyer;belt;conveyor;belt;conveyer;conveyor;transporter","canopy","washer;automatic;washer;washing;machine","plaything;toy","swimming;pool;swimming;bath;natatorium","stool","barrel;cask","basket;handbasket","waterfall;falls","tent;collapsible;shelter","bag","minibike;motorbike","cradle","oven","ball","food;solid;food","step;stair","tank;storage;tank","trade;name;brand;name;brand;marque","microwave;microwave;oven","pot;flowerpot","animal;animate;being;beast;brute;creature;fauna","bicycle;bike;wheel;cycle","lake","dishwasher;dish;washer;dishwashing;machine","screen;silver;screen;projection;screen","blanket;cover","sculpture","hood;exhaust;hood","sconce","vase","traffic;light;traffic;signal;stoplight","tray","ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin","fan","pier;wharf;wharfage;dock","crt;screen","plate","monitor;monitoring;device","bulletin;board;notice;board","shower","radiator","glass;drinking;glass","clock","flag"]
files = glob.glob(r'/Users/divyachandana/Documents/NJIT/work/summertasks/jun1-jun5/atlanta/*.jpg')
print("Total Files",len(files))
columns = ['filename','class','total_pixel','individual_pixel','ratio','timestamp']
# ---------- drive code -----
with open('semantic_results_atlanta.csv','a') as csvfile:
csvwriter = csv.writer(csvfile,lineterminator='\n')
# csvwriter.writerow(columns)
# i=0
for f in files:
file_check_query = "select count(*) from {} where filename like '%{}%'".format('semantic_results_atlanta', os.path.basename(f))
# print(file_check_query)
# i += 1
# print(i)
count = dbms.get_count_result(file_check_query)
# print(count)
if count > 0: continue
# print('resuming',f)
try:
img = image.imread(f)
img = image.resize_short(img, 1024)
# img = image.resize_short(img, 100)
# print("filename: ", f)
# ctx = mx.gpu(0)
img = test_transform(img, ctx)
# print("img: ", img)
output = model.predict(img)
# print("output: ", output)
predict = mx.nd.squeeze(mx.nd.argmax(output, 1)).asnumpy()
# print("predict: ", predict)
mask = get_color_pallete(predict, 'ade20k')
# predict.save('predict.png')
# mmask = mpimg.imread('output.png')
predict = predict.astype(numpy.uint8)
convert_single_array = numpy.array(predict)
unique_numbers = numpy.unique(convert_single_array)
# print(unique_numbers)
new_basename = os.path.basename(f).replace(".jpg", ".png")
new_name = os.path.join('output/', new_basename)
mask.save(new_name)
# color_img = image.imread(new_name)
# colors, counts = numpy.unique(color_img.reshape(-1, 3), return_counts=True, axis=0)
total_pixel = numpy.sum(predict)
d_file = drive.CreateFile({'parents': [{'id': file_id}], 'title': os.path.basename(new_name)})
d_file.SetContentFile(new_name)
d_file.Upload()
# print('Created file %s with mimeType %s' % (d_file['title'], d_file['mimeType']))
combile_all_csv_data = []
combine_sql_srting_format = []
for i in unique_numbers:
individual_count = numpy.sum(predict == i)
# print(individual_count)
csv_data = []
csv_data.append(os.path.basename(f))
csv_data.append(classes[i])
csv_data.append(total_pixel)
csv_data.append(individual_count)
csv_data.append(round((individual_count/total_pixel),6))
time_stamp = datetime.now()
csv_data.append(time_stamp)
# csv_data = [filename,predict,colors,counts,total_pixel]
# print(csv_data)
combile_all_csv_data.append(csv_data)
sql_srting = ["NULL" if val == None else "'"+str(val)+"'" for val in csv_data]
sql_srting_format = ",".join([str(val) for val in sql_srting])
combine_sql_srting_format.append(sql_srting_format)
csvwriter.writerows(combile_all_csv_data)
dbms.insertmany_sqlite3('semantic_results_atlanta',','.join(columns),combine_sql_srting_format)
os.remove(new_name)
# if idx % 10 == 0:
# print("Processed: ", idx)
except Exception as e:
print("Error in :", ' ' + f, e)
continue
print('Finished')
end = timer()
print('Processing time: %.1f' % (end - start))
